Results from the `pyperformance <https://github.com/python/pyperformance>`__
benchmark suite report
-`4-5% <https://raw.githubusercontent.com/facebookexperimental/free-threading-benchmarking/refs/heads/main/results/bm-20260110-3.15.0a3%2B-aa8578d-JIT/bm-20260110-vultr-x86_64-python-aa8578dc54df2af9daa3-3.15.0a3%2B-aa8578d-vs-base.svg>`__
+`5-6% <https://doesjitgobrrr.com/run/2026-03-11>`__
geometric mean performance improvement for the JIT over the standard CPython
interpreter built with all optimizations enabled on x86-64 Linux. On AArch64
macOS, the JIT has a
-`7-8% <https://raw.githubusercontent.com/facebookexperimental/free-threading-benchmarking/refs/heads/main/results/bm-20260110-3.15.0a3%2B-aa8578d-JIT/bm-20260110-macm4pro-arm64-python-aa8578dc54df2af9daa3-3.15.0a3%2B-aa8578d-vs-base.svg>`__
+`8-9% <https://doesjitgobrrr.com/run/2026-03-11>`__
speedup over the :ref:`tail calling interpreter <whatsnew314-tail-call-interpreter>`
with all optimizations enabled. The speedups for JIT
builds versus no JIT builds range from roughly 15% slowdown to over